Towards a Cognitive Model of MI-BCI User Training

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) enable users to control applications using their brain activity alone, by realising mental-imagery tasks. Although promising, MI-BCIs remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. We claim that understanding and improving the user-training process could greatly improve users' MI-BCI control abilities. Yet, to better understand the training process, we need a model of the factors impacting MI-BCI performance. In other words, we need to understand which traits and states impact MI-BCI performance, how these factors interact and how to influence them to improve this performance. Such a model would enable us to design adapted and adaptive training protocols, to guide neurophysiological analyses or design informed classi-fiers, among others. In this paper we propose a theoretical model of MI-BCI tasks, which is the first step towards the design of this full cognitive and computational model.

[1]  Eric Chown,et al.  Cognitive Modeling , 2014, Computing Handbook, 3rd ed..

[2]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[3]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[4]  B. Allison,et al.  The Asilomar Survey: Stakeholders’ Opinions on Ethical Issues Related to Brain-Computer Interfacing , 2011, Neuroethics.

[5]  F Lotte,et al.  Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates. , 2016, Progress in brain research.

[6]  Brendan Z. Allison,et al.  Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.

[7]  内藤 玄造,et al.  音の空間情報を用いた Auditory Brain-Computer Interface の基礎的検討 , 2012 .

[8]  Willem F. G. Haselager,et al.  BCI and a User’s Judgment of Agency , 2014 .

[9]  Mar ianne Mise,et al.  Children Who Do Well in School : Individual Differences in Perceived Competence and Autonomy in Above-Average Children , 2001 .

[10]  Raymond B. Cattell,et al.  Personality Structure and the New Fifth Edition of the 16PF , 1995 .

[11]  F Nijboer,et al.  Technology transfer of brain-computer interfaces as assistive technology: barriers and opportunities. , 2015, Annals of physical and rehabilitation medicine.

[12]  Fabien Lotte,et al.  Peanut: Personalised Emotional Agent for Neurotechnology User-Training , 2017, GBCIC.

[13]  J. Wolpaw,et al.  Brain-Computer Interfaces: Principles and Practice , 2012 .

[14]  N. Birbaumer,et al.  Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. , 2001, Archives of physical medicine and rehabilitation.

[15]  Aleksandra Vuckovic,et al.  Using a motor imagery questionnaire to estimate the performance of a Brain–Computer Interface based on object oriented motor imagery , 2013, Clinical Neurophysiology.

[16]  S. Vandenberg,et al.  Mental Rotations, a Group Test of Three-Dimensional Spatial Visualization , 1978, Perceptual and motor skills.

[17]  Moritz Grosse-Wentrup.  What are the Causes of Performance Variation in Brain-Computer Interfacing? , 2011 .

[18]  Klas Ihme,et al.  A public data hub for benchmarking common brain–computer interface algorithms , 2011, Journal of neural engineering.

[19]  Arnaud Delorme,et al.  Meditation and neurofeedback , 2013, Front. Psychol..

[20]  Bernhard Schölkopf,et al.  Towards neurofeedback for improving visual attention , 2013 .

[21]  J. Keller.  Motivational Design for Learning and Performance: The ARCS Model Approach , 2009 .

[22]  Nur’ain Achim,et al.  Computer usage: the impact of computer anxiety and computer self- efficacy , 2015 .

[23]  Sriram Subramanian,et al.  Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns , 2015, PloS one.

[24]  Steven E. Poltrock,et al.  Individual Differences in visual imagery and spatial ability , 1984 .

[25]  Mark J. Brosnan,et al.  The impact of computer anxiety and self-efficacy upon performance , 1998, J. Comput. Assist. Learn..

[26]  P. Ackerman.  Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. , 1988 .

[27]  Fabien Lotte,et al.  Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study , 2016, Journal of neural engineering.

[28]  N. Birbaumer,et al.  Predictors of successful self control during brain-computer communication , 2003, Journal of neurology, neurosurgery, and psychiatry.

[29]  Fabien Lotte,et al.  Online Classification accuracy is a Poor Metric to Study Mental imagery-based BCI User Learning: an Experimental Demonstration and New Metrics , 2017, GBCIC.

[30]  Jacqueline Bourdeau,et al.  Advances in Intelligent Tutoring Systems , 2010 .

[31]  Ali Şimşek.  The Relationship between Computer Anxiety and Computer Self-Efficacy , 2011 .